Rational Abandonment from Tele-Queues: Nonlinear Waiting Costs with Heterogeneous Preferences

We consider the modelling of abandonment from a queueing system by impatient customers. Within the proposed model, customers act rationally to maximise a utility function that weights service utility against expected waiting cost. Customers are heterogeneous, in the sense that their utility function parameters may vary across the customer population. The queue is assumed invisible to waiting customers, who do not obtain any information regarding their standing in the queue during their waiting period. Such circumstances apply, for example, in telephone centers or other remote service facilities, to which we refer as tele-queues. We analyse this decision model within a multi-server queue with impatient customers, and seek to characterise the Nash equilibria of this system. These equilibria may be viewed as stable operating points of the system, and determine the customer abandonment profile along with other system-wide performance measures. We provide conditions for the existence and uniqueness of the equilibrium, and suggest procedures for its computation. We also suggest a notion of an equilibrium based on sub-optimal decisions, the myopic equilibrium, which enjoys favourable analytical properties. Some concrete examples are provided to illustrate the modelling approach and analysis. The present paper supplements previous ones which were restricted to linear waiting costs or homogeneous customer population.     

Modelling length of stay and patient flows: methodological case studies from the UK neonatal care services

The number of babies needing neonatal care is increasing mainly because of technological and therapeutic advances. These advances have implied a decreasing neonatal mortality rate for low birth weight infants and also a falling incidence of preterm stillbirth. Given the structural changes in the National Health Service in England, coupled with recession and capacity constraints, the neonatal system is facing some serious challenges, such as nurse shortages and the lack of cots, which could inevitably impact neonates’ length of stay, and the performance of the system as a whole. These constraints have forced neonatal managers to better understand their organisation and operations in order to optimise their systems. As a result, we have developed three unique methodologies based on length of stay modelling, physical patient pathways, and system dynamics modelling. This paper evaluates these techniques applied to neonatal services in London and showcases their usefulness and implications in practice, particularly focusing on patient flow to determine major drivers of the system, which could reduce inefficiencies, improve patient experience, and reduce cost.     

On the approximation of empirical data for service system simulations

The difficulties associated with parameter estimation for phase-type approximations of empirical data distributions in queue modelling are well known. While significant progress has been achieved in improving such approximations, difficulties in parameter estimation still limit the extent to which queue modelling is applied in practice. This paper presents a simplified technique for approximating empirical data in service system simulations, based on the specialised Cox phase-type distribution. When utilised in simulation modelling of a service system, the specialised Cox distribution is shown to provide improved approximations to the combined waiting and service time distribution without the need for complex parameter estimation techniques. This approach should enable much greater flexibility in the application of queue modelling to service systems.     

Administrative days in acute care facilities: a queueing-analytic approach.

After acute care services are no longer required, a patient in an acute care hospital often must remain there while he or she awaits the provision of extended care services by a nursing home, through social support services, or by a home health care service. This waiting period is often referred to as "administrative days" because the time is spent in the acute facility not for medical reasons, but rather for administrative reasons. In this paper we use a queueing-analytic approach to describe the process by which patients await placement. We model the situation using a state-dependent placement rate for patients backed up in the acute care facility. We compare our model results with data collected from a convenience sample of 7 hospitals in New York State. We conclude with a discussion of the policy implications of our models.     

Optimal Sequencing of Servers in the Two-Station Series Queueing System

This paper deals with determining an optimal sequence of service stations in a series queueing system. Optimality is defined in terms of the total time spent waiting for service. Sequences are compared on the basis of the moments of their steady-state total waiting time. In addition, the rules of stochastic dominance are applied which allow comparison of sequences on the basis of their waiting time distributions. Analytical results in the sequencing of service stations in series queues have been limited to stations with constant or exponential service times. This study extends the investigation to service distributions with varying degrees of statistical regularity given by the family of Erlang distributions.Relationships are developed for predicting optimal sequences. Validation is accomplished by simulating a number of systems and comparing the waiting time distribution functions for each sequence. The relationships are shown to be good predictors and useful in the study and design of systems of servers in series.     

Location of services and the impact on healthcare quality: insights from a simulation of a musculoskeletal physiotherapy service

Many healthcare systems are being redesigned to deliver local care with more services within the community. Relocation may enhance access but other aspects of healthcare quality should also be considered, notably waiting times and equity of care. This study examined a musculoskeletal physiotherapy service using a discrete-event simulation with simple heuristics to model patient behaviour. This combination provided an effective mechanism for incorporating the individuality of the patients in the flows along the patient pathways, subject to the varying availabilities of key resources. In particular, it captured the feedback that is critical in system performance, especially where waiting times are important. The model recognised the heterogeneity of patient attitudes and demonstrated how the behaviour of a relatively small proportion can affect the experience of all patients. The study suggested that, with careful operational management, more care could be delivered locally while exploiting many of the benefits of a centralised service.     

Simulation based analysis of patient arrival to health care systems and evaluation of an operations improvement scheme

Various demands of different patients over both medical resource and time domains in health care systems raise requests of strategies for balanced system capacity from an operations perspective. In this paper, a quantitative modeling technique with both patient arrival and associated treatment process integrated are used to characterize health care system performance and evaluate system efficiency. The patient arrival process is described as a dynamic random Poisson process and patient treatments are characterized as consumption processes of various health care resources over time with a view of the “product line” used. The waiting time of patients and usage of health care resources are proposed as system performance measures based on their means, variances, and confidence intervals. A simulation considering patients with several various diseases is given to find a mechanism of conflicting factors in decisions of balanced system capacity, and an operation scheme of “evenly balanced load for bottlenecks” is obtained based on analysis of simulation outputs. Simul8 provides the software environment for the simulation.     

Two‐tier healthcare service systems and cost of waiting for patients

Waiting has been a significant concern for healthcare services. We address this issue in the context of a two‐tier service system in this study. A two‐tier healthcare service system consists of two different service providers, typically one public service provider and one private service provider. In a baseline model, the two service providers are modeled by two queue servers, which charge each patient a common fixed fee for the service. Then, we study a queue model in which one service provider offers a subsidy or charges a premium while the other maintains the fixed service fee. This system provides a mechanism to segment patients along their waiting time cost through price discrimination. We analyze the problem from both the perspective of minimizing total waiting cost for all patients and the perspective of maximizing social gain for the public service provider or profit for the private service provider. We show that this model can significantly alleviate the burden of waiting for patients. The study addresses the design, the efficiency, and the implementation of two‐tier healthcare service systems. Copyright © 2017 John Wiley & Sons, Ltd.     

An evolutionary approach to rehabilitation patient scheduling: A case study

Focusing on real settings, this study aimed to develop an evolutionary approach based on genetic algorithm for solving the problem of rehabilitation patient scheduling to increase service quality by reducing patient waiting time and improve operation efficiency by increasing the therapy equipment utilization. Indeed, due to partial precedence constraints of rehabilitation therapies, the problem can be structured as a hybrid shop scheduling problem that has received little attention to date. In addition, a mixed integer programming model was also constructed as a benchmark to validate the solution quality with small problems. Based on empirical data from a Medical Center in Taiwan, several experiments were conducted to estimate the validity of the proposed algorithm. The results showed that the proposed algorithm can reduce patient waiting time and enhance resource utilization and thus demonstrated the practicality of the proposed algorithm. Indeed, a decision support system embedded with the developed algorithm has been implemented in this medical center.     

The optimal allocation of server time slots over different classes of patients

We present a model for assigning server time slots to different classes of patients. The objective is to minimize the total expected weighted waiting time of a patient (where different patient classes may be assigned different weights). A bulk service queueing model is used to obtain the expected waiting time of a patient of a particular class, given a feasible allocation of service time slots. Using the output of the bulk service queueing models as the input of an optimization procedure, the optimal allocation scheme may be identified. For problems with a large number of patient classes and/or a large number of feasible allocation schemes, a step-wise heuristic is developed. A common example of such a system is the allocation of operating room time slots over different medical disciplines in a hospital.     

Differentiated waiting time management according to patient class in an emergency care center using an open Jackson network integrated with pooling and prioritizing

Unlike ordinary outpatient clinics, an emergency care center sees a variety of patients with diverse diseases and injuries of different levels of severity. Since patients who are in a critical condition face serious consequences, target waiting times must be determined based on patient acuity levels. To reflect the special situation in emergency care centers included in this study, patient flows are formulated using an open Jackson network with multiple patient classes. This paper is unique because of the integration of pooling and prioritizing patient classes with the open Jackson network. In particular, a hybrid priority model is presented in which a first-come-first-served discipline is applied in some processes and a priority discipline is applied in other processes in the open Jackson network, in order to minimize waiting times for patients with more urgent concerns. A case study based on actual data from an emergency care center demonstrates that the proposed model of pooling and prioritizing patient classes is effective in decreasing waiting times for higher-priority classes without substantially sacrificing those for lower-priority classes.     

Enabling better management of patients: discrete event simulation combined with the STAR approach

Squeezed budgets and funding cuts are expected to become a feature of the healthcare landscape in the future, forcing decision makers such as service managers, clinicians and commissioners to find effective ways of allocating scarce resources. This paper discusses the development of a decision support toolkit (DST) that facilitates the improvement of services by identifying cost savings and efficiencies within the pathway of care. With the help of National Health Service and commercial experts, we developed a discrete event simulation model for deep vein thrombosis (DVT) patients and adapted the socio-technical allocation of resources (STAR) approach to answer crucial questions like what sort of interventions should we spend our money on? Where will we get the most value for our investment? How will we explain the choices we have made? The DST enables users to model their own services by working with the DST interface allowing users to specify local DVT services. They can input local estimates, or data of service demands and capacities, thus creating a baseline discrete event simulation model. The user can then compare the baseline with potential changes in the patient pathway in the safety of a virtual environment. By making such changes key decision makers can easily understand the impact on activity, cost, staffing levels, skill-mix, utilisation of resources and, more importantly, it allows them to find the interventions that have the highest benefit to patients and provide best value for money.     

A simulation study of scheduling clinic appointments in surgical care: individual surgeon <Emphasis Type="Italic">versus</Emphasis> pooled lists

The purpose of this paper is to compare two methods of scheduling outpatient clinic appointments in the setting where the availability of surgeons for appointments depends on other clinical activities. We used discrete-event simulation to evaluate the likely impact of the scheduling methods on the number of patients waiting for appointments, and the times to appointment and to surgery. The progression of individual patients in a surgical service was modelled as a series of updates in patient records in reaction to events generated by care delivery processes in an asynchronous fashion. We used the Statecharts visual formalism to define states and transitions within each care delivery process, based on detailed functional and behavioural specifications. Our results suggest that pooling referrals, so that clinic appointments are scheduled with the first available surgeon, has a differential impact on different segments of patient flow and across surgical priority groups.     

The multi-hour service system design problem

This paper studies the multi-hour service system design problem that involves locating service facilities, determining their number and capacities and assigning user nodes to those facilities under time varying demand conditions. The goal is to minimize total costs made of the costs of accessing facilities by users and waiting for service at these facilities as well as the cost of setting up and operating the facilities. A unified model is proposed for the problem to exploit the benefits of the non-coincidence of demand across busy-hours. Two Lagrangean relaxation-based heuristic solution procedures are developed and the results of extensive computational experiments are reported.     

Performance Monitoring and Competence Assessment in Health Services

Health and health service monitoring is among the most promising research area today and the world work towards efficient and cost effective health care. This paper deals with monitoring health service performance using more than one performance outcome variable (multi-attribute processes), which is common in most health services. Although monitoring whether a health service changes or improves over time is important this is well covered in the current literature. Therefore this paper focuses on comparing similar health services in terms of their performance. The proposed procedure is based on an appropriate control chart. The paper deals with firstly the case when no risk adjustment is required because the health services being compared treat the same patient case-mix which does not vary over time. Secondly it deals with comparing health services where risk adjustment is required because the patient case-mix they service do differ because they service either very different geographical locations or service very different demographics of the same population. The technology developed in this paper could be used for example to assess and compare health practitioners’ competence over time, i.e. to decide if two doctors are equivalent in terms of their outcome performances. The waiting time random variable associated with the run length distribution of the control charts (as well as to competence testing) is studied using a Markov Chain embedding technique. Numerical results are provided that exhibit the value of the proposed procedures.     

Modelling variations in hospital service delivery based on real time locating information

Variations in service delivery have been identified as a major challenge to the success of process improvement studies in service departments of hospital such as radiology. Largely, these variations are due to inherent system level factors, i.e., system variations such as unavailability of resources (nurse, bed, doctors, and equipment). These system variations are largely unnecessary/unwarranted and mostly lead to longer waiting times, delays, and lowered productivity of the service units. There is limited research on identifying system variations and modelling them for service improvements within hospital. Therefore, this paper proposes a modelling methodology to model system variations in radiology based on real time locating system (RTLS) tracking data. The methodology employs concepts from graph theory to identify and represent system variations. In particular, edge coloured directed multi-graphs (ECDMs) are used to model system variations which are reflected in paths adopted by staff, i.e., sequence of rooms/areas traversed while delivering services. The main steps of the methodology are: (i) identifying the most standard path followed by staff for service delivery; (ii) filtering the redundant events in RTLS tracking database for analysis; (iii) identifying rooms/areas of hospital site involved in the service delivery; (iv) determining patterns of paths adopted by staff from filtered tracking database; and, (v) representation of patterns in graph based model called as edge coloured directed multigraphs (ECDMs) of a role. A case study of MR scanning process is utilized to illustrate the implementation of the proposed methodology for modelling system variations reflected in the paths adopted by staff.     

Emergency and on-demand health care: modelling a large complex system

This paper describes how system dynamics was used as a central part of a whole-system review of emergency and on-demand health care in Nottingham, England. Based on interviews with 30 key individuals across health and social care, a ‘conceptual map’ of the system was developed, showing potential patient pathways through the system. This was used to construct a stock-flow model, populated with current activity data, in order to simulate patient flows and to identify system bottle-necks. Without intervention, assuming current trends continue, Nottingham hospitals are unlikely to reach elective admission targets or achieve the government target of 82% bed occupancy. Admissions from general practice had the greatest influence on occupancy rates. Preventing a small number of emergency admissions in elderly patients showed a substantial effect, reducing bed occupancy by 1% per annum over 5 years. Modelling indicated a range of undesirable outcomes associated with continued growth in demand for emergency care, but also considerable potential to intervene to alleviate these problems, in particular by increasing the care options available in the community.     

Using phase-type models to cost stroke patient care across health,social and community services

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.     

Efficiency and welfare implications of managed public sector hospital waiting lists

A queuing model for public health service waiting lists is developed, and the implications for patient welfare of different systems for managing the waiting list are analysed. If patients are admitted to hospital on a first-come-first-served basis, a welfare gain is achieved by moving from a system of implicit to one of explicit rationing of access to the waiting list. If individual waiting times and hospital admissions are dependent on clinical priority, a further welfare gain is achievable without the use of explicit rationing, by reallocating the total waiting time from the more towards the less seriously ill. On efficiency and welfare criteria, a maximum waiting time guarantee does not appear to be a desirable development.